Predicting traditional Chinese medicine constitutions in adults aged ≥ 65 years:A machine learning approachOA
Predicting traditional Chinese medicine constitutions in adults aged ≥ 65 years:A machine learning approach
Chen Sun;Xiang-long Xu;Zhen Yu;Zong-yuan Ge;Wen-jun Wang;Bi-ying Wang;Hua-ling Song;Guo-qun Xie;Hai-lei Zhao;Yang Zhang
School of Public Health,Shanghai University of Traditional Chinese Medicine,Shanghai 201203,ChinaSchool of Public Health,Shanghai University of Traditional Chinese Medicine,Shanghai 201203,China||School of Translational Medicine,Faculty of Medicine,Nursing and Health Sciences,Monash University,Melbourne 3800,Victoria,Australia||Artificial Intelligence and Modelling in Epidemiology Program,Melbourne Sexual Health Center,Alfred Health,Melbourne 3053,Victoria,Australia||Bijie Municipal Center for Disease Control and Prevention,Bijie 551700,Guizhou Province,China||Bijie Institute of Shanghai University of Traditional Chinese Medicine,Bijie 551700,Guizhou Province,ChinaMonash e-Research Center,Faculty of Engineering,Monash University,Melbourne 3800,Victoria,AustraliaMonash e-Research Center,Faculty of Engineering,Monash University,Melbourne 3800,Victoria,AustraliaCentral Clinical School,Faculty of Medicine,Nursing and Health Sciences,Monash University,Melbourne 3800,Victoria,AustraliaSchool of Public Health,Shanghai University of Traditional Chinese Medicine,Shanghai 201203,China||Three Gorges University Hospital of Traditional Chinese Medicine & Yichang Hospital of Traditional Chinese Medicine,Yichang 443000,Hubei Province,ChinaSchool of Public Health,Shanghai University of Traditional Chinese Medicine,Shanghai 201203,ChinaSchool of Public Health,Shanghai University of Traditional Chinese Medicine,Shanghai 201203,ChinaSchool of Public Health,Shanghai University of Traditional Chinese Medicine,Shanghai 201203,ChinaYu Garden Community Health Care Center,Shanghai 200010,China
Traditional Chinese medicine constitutionAgedMachine learning
Traditional Chinese medicine constitutionAgedMachine learning
《结合医学学报（英文版）》 2026 (1)
98-104,7
This work was supported by the National Key R&D Program of China(2025YFC3507503),the Traditional Chinese Medicine Research Project of Shanghai Municipal Health Commission(20240N108),and the project on artificial intelligence-driven reform of scientific research paradigms to empower disciplinary advancement.
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